information and noise in financial markets: evidence …

The Journal of Financial Research • Vol. XXXI, No. 3 • Pages 247–270 • Fall 2008

INFORMATION AND NOISE IN FINANCIAL MARKETS: EVIDENCEFROM THE E-MINI INDEX FUTURES

Alexander KurovWest Virginia University

Abstract

I examine the informational contributions and effects on transitory volatility oftrades initiated by different types of traders in three actively traded index futuresmarkets. The results show that trades initiated by exchange member firms accountfor more than 60% of price discovery during the trading day. These institutionaltrades appear to be more informative than trades of individual exchange membersor off-exchange traders. I also find that off-exchange traders introduce more noiseinto the prices than do exchange members. My findings provide new evidence onthe role of different types of traders in the price formation process.

JEL Classification: G10, G14

I. Introduction

I examine the roles of different types of traders in the price discovery processand their effect on transitory volatility in index futures markets. First, I analyzethe relative information content of trades initiated by individual exchange mem-bers, exchange member firms, and off-exchange traders in three E-mini index fu-tures markets. Kurov and Lasser (2004) use Hasbrouck’s (1995) information shareapproach to show that individual exchange members make larger informationalcontributions than off-exchange traders or clearing member firms. Consistent withKurov and Lasser, I find that the informational contributions of off-exchange tradersare relatively small. However, the results also show that exchange member firmscontribute significantly more to price discovery than do individual members or off-exchange traders. Trades of exchange member firms appear to be more informativethan trades of other traders. These results show that institutional traders now playa dominant informational role in electronic index futures markets.

I thank Lou Abarcar, Arabinda Basistha, Robert Daigler (the referee), Upinder Dhillon, Grigori Eren-burg, Gerald Gay (the editor), Andrew Harvey, Dennis Lasser, Peter Locke, Stephen Satchell, and RobertWebb for helpful comments and suggestions. Special thanks to Alessio Sancetta for many helpful dis-cussions. I am also grateful to the staff of the Commodity Futures Trading Commission for their help inobtaining the data. Any remaining errors are my own.

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248 The Journal of Financial Research

Second, I analyze the effect of trading activity initiated by different typesof futures traders on transitory volatility. I find that trades initiated by off-exchangetraders are positively related to transitory volatility, whereas the similar relationfor individual and institutional exchange member trades is negative. This finding,supported by results from a decomposition of transitory volatility, shows that off-exchange traders introduce more noise into the prices than do exchange members.

My analysis of transitory volatility extends the work of Daigler and Wiley(1999), who show that the positive volume–volatility relation is determined by off-exchange traders, whereas trading volumes of exchange clearing members tend tohave a negative relation with volatility. They conclude that off-exchange traders aremore likely to trade on noise and cause excess volatility than exchange members,who are more informed. However, when returns are sampled at daily intervals, asin Daigler and Wiley, the estimated volatility may be dominated by informationflow.1 Therefore, the results of Daigler and Wiley may be explained by differentialcontributions of trader types to the price discovery process rather than by noisetrading. My results are broadly consistent with theirs but provide direct evidenceon the relation between noise volatility and trading activity classified by trader type.

I consider the S&P 500, NASDAQ-100, and Russell 2000 E-mini indexfutures markets for several reasons. First, the trader type classifications availablein futures trade data allow studying the effects of proprietary trading by exchangemember firms. Such institutional traders play an important intermediary role onmany securities exchanges. Second, the E-mini contracts trade on an electronic limitorder market that is similar to the trading systems that now account for most of thesecurities trading around the world. Therefore, my results can be generalized to otherelectronic markets. Finally, an analysis of the E-mini markets allows examining theafter-hours trading period, when the major stock markets are closed.

Overall, my findings contribute to a better understanding of the roles ofdifferent types of traders in price formation. I find that off-exchange traders accountfor a disproportional share of noise, whereas institutional traders introduce lessnoise and bring more information to the market. Institutional traders are better ableto exploit their informational advantages in anonymous electronic trading than intraditional floor markets. Therefore, given the continuing transition to electronictrading in the U.S. securities markets, we can expect an increasing informationaldominance of institutions across a variety of markets.

1Speight, McMillan, and Ap Gwilym (2000) examine the component structure of volatility in theFTSE 100 index futures market. They show that the transitory component dominates the volatility processover intraday frequencies, whereas the long-run component dominates over daily and lower frequencies.They argue that the long-run volatility component is determined by information flow, and the transitorycomponent may be induced by trading activity.

Information and Noise in Financial Markets 249

II. Literature Review

Price Formation and Noise Trading

Price information is perhaps the single most important product of financial markets.Informative prices reflect the underlying asset values and contribute to efficientallocation of resources in the economy. French and Roll (1986) provide evidence thatprivate information, which is reflected in the prices through the trading of informedtraders, accounts for most of the information affecting stock prices. Security pricescontain information, but they also contain noise induced by market frictions andnoise trading. Grossman and Stiglitz (1980) and Kyle (1985) offer a theoreticalrationale for existence of noise trading by showing that noise is necessary forinformed traders to profit from their information. Noise traders facilitate the price-discovery process by providing camouflage to informed traders. As a result, bothinformation and noise end up reflected in the security prices.

The interplay between informed and noise traders in the price formationprocess has been extensively studied theoretically but remains underexplored inempirical work. One difficulty in addressing this issue empirically is that, as Black(1986) notes, it is unclear who is informed and who is a noise trader. Informationabout the type of trader behind a particular transaction is generally not publicly avail-able. Existing equity market research (e.g., Chakravarty 2001) provides evidencethat institutions, as opposed to retail traders, are informed traders. The apparentinformational advantage of institutional traders may stem from their superior ana-lytical resources or privileged access to information. Kurov and Lasser (2004) andAnand and Subrahmanyam (2008) analyze how trading activity of different typesof financial traders affects the price formation. My article contributes to this strandof literature.

Information Asymmetry in Futures Markets

I examine informational contributions of different trader types in index futuresmarkets. Therefore, it is important to consider the possible nature of informationasymmetry in the context of my study. Microstructure theories usually assume thatinformed traders are insiders with private information about the firm’s cash flows.Security-specific private information is unlikely to be an important source of in-formation asymmetry in index futures markets because such information becomesdiversified in broad market indexes (e.g., Subrahmanyam 1991). However, Chan(1992) argues that index futures represent a low-cost instrument for trading on mar-ketwide information. Some traders are likely to be better than others at interpretingpublic information or simply able to react to macroeconomic information fasterthan other traders. For example, Erenburg, Kurov, and Lasser (2006) show thatexchange locals trading the E-mini S&P 500 index futures make profitable tradesimmediately after scheduled macroeconomic announcements.


Existing research shows that trading based on heterogeneous interpretationof public information plays an important role in price discovery in markets whereprivate information in its common sense does not exist. For example, Evans andLyons (2002) and Brandt and Kavajecz (2004) find that order flow contains in-formation that determines foreign exchange rates and U.S. Treasury bond yields.Studies of futures markets by Kurov and Lasser (2004) and Locke and Onayev(2007) show that access to customer order flow ensures that exchange locals havean informational advantage over off-floor traders.

Volume–Volatility Relation

Numerous empirical studies document a positive contemporaneous correlation be-tween trading volume and price volatility. Several theories explain the volume-volatility relation. The mixture of distributions hypothesis (MDH) was originallyproposed by Clark (1973) and extended by Epps and Epps (1976) and Tauchenand Pitts (1983). According to MDH, both price volatility and trading volume aredriven by the unobserved information arrival process.

In the strategic trading model of Admati and Pfleiderer (1988), contempo-raneous correlation between volume and volatility arises from behavior of liquiditytraders who bunch their trades in time to reduce their losses to informed traders.Informed traders find it optimal to trade more in intervals of high uninformedtrading activity because this activity allows them to hide their trades. Their privateinformation is impounded into the prices, leading to higher price volatility duringperiods of high trading activity.

I do not test a particular theoretical model. However, the model most closelyrelated to my research is the noisy rational expectations model of a futures marketdeveloped by Shalen (1993). In this model hedgers trade for liquidity reasons andspeculators attempt to extract information from observed price changes but are un-able to distinguish between price changes caused by information and price changescaused by trades of hedgers. This confusion increases dispersion of expectations,leading to excess price volatility, increased trading volume, and contemporaneouscorrelation between volume and volatility. Daigler and Wiley (1999) provide empir-ical evidence supporting this model by showing that the volume–volatility relationin futures markets is driven by off-exchange traders.

III. Background and Hypotheses

Futures markets offer an excellent environment to analyze the effects of traderheterogeneity on price formation because of availability of data with trader typeclassifications. The three major types of traders identified in such data include in-dividual exchange members, also called “locals”; exchange member firms making


proprietary trades; and off-exchange traders. Exchange members act as interme-diaries and de facto market makers (e.g., Chang and Locke 1996). They enjoyexclusive access to open outcry trading, as well as substantially lower trading andclearing fees. Such trading privileges are commonly justified by the role of thesetraders in providing liquidity.

The E-mini futures contracts trade on the GLOBEX electronic trading sys-tem operated by the Chicago Mercantile Exchange (CME). These contracts aresized at one-fifth of the respective floor-traded contracts. With combined aver-age daily trading volumes exceeding 1 million contracts, the E-minis are perhapsthe most successful contracts in the CME’s history. CME local traders actively tradethe E-mini futures. Some of these trades are made from GLOBEX terminals locatedin the equity quadrant on the CME floor. Kurov and Lasser (2004) show that tradesinitiated by locals are more informative than off-exchange initiated trades. Theyfind that locals make well-timed trades in GLOBEX around large floor trades,suggesting that locals are able to use their access to the order flow informationfrom the open outcry floor to take positions in the simultaneous electronic market.Kurov and Lasser argue that the resulting transfer of information from the floorto GLOBEX may contribute to the informational dominance of the E-mini futuresdocumented by Hasbrouck (2003).

My analysis of GLOBEX transactions data shows that CME’s memberfirms account for a large proportion of the E-mini trading activity. These largeinstitutions trade primarily for hedging and speculative reasons. Information gen-erated by in-house analysis, as well as analysis of the order flow of their customers,affects the trading strategies of these proprietary traders. Member firms have sub-stantial analytical resources, a real-time access to information about the cash market,knowledge of customer order flow, and access to the trading floor. Large institu-tional traders also have the resources to develop and maintain algorithmic tradingsystems, which generate a growing share of trading activity in index futures mar-kets.2 Algorithmic trading systems reduce execution costs by minimizing the priceimpact of trades and can respond to electronic information from newswires muchfaster than human traders. These advantages are likely to ensure that exchange mem-ber firms are better informed relative to most off-exchange traders and individualexchange members.

Off-exchange traders are smaller institutions and retail customers of bro-kerage firms. These traders do not have direct access to the trading floor, havelimited analytical resources, and often make trading decisions based on tech-nical analysis (i.e., interpretation of recent price patterns and trading activity).Given their informational advantages, exchange members are likely to induce less

2Futures exchanges have been experiencing rapid growth in algorithmic trading. Algorithmic tradingsystems give institutional traders an advantage over retail traders (“Small traders face losing calculus,” WallStreet Journal, February 21, 2007, p. E5).


transitory volatility and make larger contributions to price discovery than off-exchange traders. These considerations lead to the following hypotheses:

H1: Trades initiated by off-exchange traders are associated with highertransitory volatility and lower informational volatility than trades ini-tiated by exchange members.

H2: Trades initiated by exchange members contribute more to price dis-covery than do off-exchange initiated trades.

IV. Data and Descriptive Statistics

I use transactions data obtained from the Commodity Futures Trading Commis-sion (CFTC). For both sides of each trade, the data contain the trade date, ordersubmission time and trade time to the nearest second, the contract month, buy/sellcode, number of contracts traded, trade price, and customer type indicator (CTI).CTI ranges from 1 to 4 as follows: CTI 1 are local trades (i.e., trades initiated andexecuted by an individual member for his or her own account), CTI 2 are tradesexecuted for a proprietary account of a member firm, CTI 3 are trades executed fora personal account of another individual member, and CTI 4 are trades executed foran account of any other trader.3 CTI 4 trades originate from off-exchange traders.CTI 3 trades account for less than 2% of the trading activity and I combine themwith CTI 1 trades into a single category for individual exchange members. Myusage of the term “exchange members” refers to individual exchange members andmember firms.

I examine 250 trading days from January 3, 2005, to December 30, 2005and consider only the most actively traded expiration in each market for everytrading day. Shortened preholiday days are removed from the sample. The sum-mary statistics of my sample are given in Table 1. Trading in the E-mini futures isextremely active, with several trade executions per second on average. The openinterest in the E-mini contracts is comparable to the daily trading volume, suggest-ing that most of the volume is generated by intraday speculative trading activity.Table 1 also shows trading activity statistics for after-hours trading.4 The overalllevel of trading activity after hours is relatively low.

3In 2004, the U.S. futures exchanges adopted a harmonized set of CTI code definitions. Before thischange in code definitions, CTI 2 was used only for proprietary accounts of clearing member firms. Basedon the new definitions, which applied in 2005, orders of non-clearing-member firms, previously codedas CTI 4, were reclassified as CTI 2. Therefore, the new CTI code definitions allow a more accurateidentification of institutional trades.

4After-hours trading starts at 4:30 p.m. and continues until 9:30 a.m. ET the following day. GLOBEXcloses at 5:30 p.m. for a 30-minute maintenance period. I omit the 4:30–5:30 p.m. period to prevent thepossible effect of the trading halt on model estimates.


TABLE 1. Summary Statistics.

E-Mini E-Mini E-MiniS&P 500 NASDAQ-100 Russell 2000

Regular trading hoursMean number of trade executions per minute 235.3 124.2 140.9Mean daily trading volume (contracts) 737,424 264,821 105,696Mean execution size (contracts) 7.74 5.26 1.85Median execution size (contracts) 2 2 1

After-hours tradingMean number of trade executions per minute 9.7 3.1 0.56Mean daily trading volume (contracts) 49,059 13,243 1,017Mean execution size (contracts) 5.42 4.54 1.96Median execution size (contracts) 2 2 1

Mean open interest (contracts) 1,015,239 339,362 213,665

Note: The regular trading hours are from 9:30 a.m. to 4:15 p.m. ET. The after-hours period is from6:00 p.m. to 9:30 a.m. ET the following day. The sample period is from January 3, 2005, to December 30,2005.

I analyze trades initiated by individual exchange members, member firms,and off-exchange traders. Using initiated trades allows me to assign each trade to aspecific trader type rather than to both counterparties in the trade, an approach usedby Daigler and Wiley (1999). It is more appropriate to examine transitory volatilityusing initiated trades, which represent liquidity demands. Given that limit ordersaccount for a significant proportion of order flow, a positive relation between thetotal trading volume of a particular trader type and volatility is also consistent withthose traders supplying liquidity to other traders in high-volatility periods. Usinginitiated trades eliminates the potential for this alternative explanation of the results.

GLOBEX is a limit order market. Incoming market orders are executedimmediately against the standing limit orders. My data set includes the order sub-mission times for both sides of each trade. Because aggressive orders are executedimmediately and limit orders tend to spend some time waiting for execution, com-paring the submission times for the two sides of each trade allows identifying theinitiating side. Specifically, I identify the initiating side by assuming that the orderwith the later submission time initiated the trade. About 99% of nonzero tick tradesare classified identically by the order submission times and the tick rule, showingthat the submission times can be used to classify trades accurately. When the sub-mission times for both sides of the trade are the same, the trade is signed using thetick rule. More than 80% of all trades are zero-tick trades. Finucane (2000) showsthat the tick rule is relatively inaccurate for zero-tick trades. Therefore, I removetrades that occurred on a zero tick and have the same reported submission times forboth sides. These trades account for less than 14% of the total number of trades.

Table 2 shows proportions of the total trading volume and the total numberof trades initiated by different trader types for both trading periods. During the


TABLE 2. Trading Activity Statistics for Trades Initiated by Individual Exchange Members (CTI 1and 3), Exchange Member Firms (CTI 2), and Off-Exchange Traders (CTI 4).

Regular Trading Hours After-Hours Trading

CTI 1 & 3 CTI 2 CTI 4 CTI 1 & 3 CTI 2 CTI 4

E-mini S&P 500

Distribution of number of tradesOverall 17.4% 45.0% 37.5% 24.6% 29.9% 45.5%Nonzero tick trades 12.2% 29.9% 58.0% 17.8% 22.9% 59.3%Zero-tick trades 18.3% 47.6% 34.0% 25.4% 30.9% 43.7%

Distribution of trading volume 20.0% 48.7% 31.3% 24.4% 33.2% 42.4%Average execution size (contracts) 8.88 8.38 6.47 5.39 6.01 5.05

E-mini NASDAQ-100



E-mini Russell 2000



Note: The regular trading hours are from 9:30 a.m. to 4:15 p.m. ET. The after-hours trading period is from6:00 p.m. to 9:30 a.m. ET the following day. The sample period is from January 3, 2005, to December30, 2005. Trades are classified by type of initiator using the reported GLOBEX order submission times.Trades with equal submission times for both sides are classified using the tick rule. Nonclassifiable trades(i.e., trades that occurred on a zero tick and have the same reported submission times for both sides) areremoved.

trading day, trades for proprietary accounts of exchange member firms account forabout half of the trading activity in all three E-mini markets. Exchange memberfirms account for a much lower percentage of the after-hours trading activity, withtheir share of trades ranging from about 29% for the E-mini Russell 2000 futuresto about 35% in the E-mini NASDAQ-100 market. The lower proportion of tradingactivity generated by member firms after hours is consistent with existence of aninstitutional trading day tied to the normal exchange trading hours (e.g., Ferguson,Mann, and Schneck 1998). Individual exchange members and off-exchange tradersalso trade actively in both trading periods. In after-hours trading, off-exchangetraders account for the largest share of trading activity.

Table 2 also reports the distribution of the number of zero-tick and non-zero-tick trades initiated by different trader types. In all three markets, individualand institutional exchange members account for a larger proportion of zero-tick


trades than of price-changing trades. The relatively small proportions of price-changing trades initiated by exchange members show that exchange members usetrading strategies that reduce the temporary price effect of their trades.

V. Empirical Tests and Results

Effect of Trading Activity on Transitory and Informational Volatility

To test my hypotheses, I use two complementary approaches: regression analysisand variance decomposition techniques. This subsection discusses the regressionresults for informational and transitory volatility. In subsections that follow I usevariance decomposition techniques that directly attribute components of the infor-mational and transitory volatility to trades initiated by different types of traders.This approach alleviates possible concerns about reverse causality in the volume–volatility relation. The two approaches provide consistent results.

Daigler and Wiley (1999) argue that their results are consistent with thenotion that noise trading leads to excess volatility. However, the positive relationof the trading volume of off-exchange traders with volatility documented in theirstudy is also consistent with a significant contribution of off-exchange traders tothe price discovery process, that is, impounding new information into the prices. Todistinguish between the effects of trading on informational and noise volatility, Ilook at the relation between these two distinct types of volatility and trading activityaggregated by type of trader. The high trading activity in the E-mini markets allowsme to estimate informational and transitory volatility in intraday intervals usingthe Hasbrouck (1993) approach. I then regress these volatility estimates on tradingactivity variables to test Hypothesis 1. The security price is represented as:

pt = mt + st , (1)

mt = mt−1 + wt , (2)

where mt is a random walk component or “efficient price” that is a conditionalexpectation of the security’s terminal value, wt are random walk innovations thatreflect new information arrivals, and st is a stationary component that representsthe pricing error (i.e., the difference between the actual trade price and the efficientprice). The variance of the pricing error is a natural measure of transitory volatility,whereas the efficient price variance represents informational volatility.

Hasbrouck (1993) shows that the variance of the pricing error and the effi-cient price variance can be estimated using a vector autoregression (VAR) of returnsand trades. The intuition for this approach is that it allows separating transitoryeffects of trades and price changes from permanent informational effects usingimpulse responses. I follow Hasbrouck’s approach to estimate the variance of the


pricing error and the efficient price variance for the regression tests. I begin byestimating a VAR using trade-by-trade data in each 15-minute interval. With ex-tremely active trading in the E-mini markets, each 15-minute interval contains asufficient number of observations to estimate the VAR, specified as follows:

[rt

xt

]=

n∑

j=1

A j

[rt− j

xt− j

]+

[vrt

vxt

], (3)

where rt are trade-by-trade returns and xt is a vector of three variables: (1) a signedtrade variable (1 for buys and −1 for sells), (2) signed trade volume, and (3) signedsquare root of the trade volume. Therefore, the VAR includes four equations: anequation for transactions returns and three equations for signed trade variables. Ai

are coefficient matrices and vt are random disturbances with a covariance matrixcov(v).5

Hasbrouck (1993) shows that the pricing error in equation (1) can be rep-resented as:

st =∞∑

j=0

α j vr ,t− j +3∑

i=1

∞∑

j=0

βi j vi ,t− j , (4)

where the α and β coefficients are calculated using the vector moving average(VMA) coefficients, which are obtained using impulse responses, that is, by fore-casting the VAR after a unit shock in one of the variables. Transitory volatility isestimated as:

σ 2s =

∞∑

j=0

[α j β j ] cov(v)

[α j

β ′j

], (5)

where β j are 1 × 3 row matrices.The variance of the random walk component (informational volatility) is

estimated as:

σ 2w =

[∑a∗

j

∑b∗

j

]cov(v)

∑

a∗j

∑b∗′

j

, (6)

where a∗j and b∗

j are VMA coefficients.

5I use 15 lags for the E-mini S&P 500 market and 10 lags for the E-mini NASDAQ-100 and E-miniRussell 2000 futures. The different numbers of lags are used to account for differences in trading activity.The regression results discussed in the text are not affected qualitatively by the number of lags in the VAR.


After estimating transitory and informational volatility for each 15-minuteintraday interval, I test Hypothesis 1 by estimating the following regression using15-minute data:

σ 2t = α + β0 AVt +

3∑

i=1

βi N Ti,t + τTrend +5∑

j=1

ρ jσ2t− j +

26∑

k=1

γk Dk,t + εt , (7)

where σ 2t is the estimate of transitory or informational volatility (σ 2

s or σ 2w ) in

15-minute interval t, AV t is the average trade size, NTi,t is the number of tradesinitiated by trader type i, Trend is a linear time trend, and Dk,t are interval dummyvariables. The interval dummies are used to control for the intraday seasonality involatility. Lagged volatility and trend are included to control for autocorrelation anda possible time trend in volatility.6 Average trade size is included to test whetherthe overall trading volume has additional explanatory power after controlling forthe number of trades classified by type of trader.7 To simplify comparison of thecoefficients, I divide the numbers of trades by their sample standard deviations.The overall specification of the model is similar to the one used by Jones, Kaul,and Lipson (1994), although they use a different measure of volatility. Equation(7) is estimated using ordinary least squares with Newey and West (1987) standarderrors.

The estimation results for transitory volatility presented in Panel A ofTable 3 follow a similar pattern for all three contract markets. The coefficients ofthe number of trades initiated by off-exchange traders are significantly positive inall cases. The similar coefficients for individual exchange members and memberfirms are negative and generally statistically significant, showing that trading activ-ity of exchange members is associated with lower transitory volatility. SupportingHypothesis 1, these results show that off-exchange traders induce more transitoryvolatility than do exchange members. The results are also consistent with the dis-tribution of zero-tick and non-zero-tick trades reported in Table 2.

The results for informational volatility presented in Panel B of Table 3 arealso consistent with Hypothesis 1. Trading activity of exchange member firms has astrong positive relation with informational volatility in all three E-mini markets. Thesimilar relation for trades of off-exchange traders is negative in the S&P 500 andNASDAQ-100 E-mini markets, and insignificant in the E-mini Russell 2000 market.The informational volatility regression results for individual exchange membersand off-exchange traders are generally consistent with Kurov and Lasser (2004),

6Five lags of volatility are included in the regression based on the Schwartz information criterion. Themodel uses all twenty-seven 15-minute intervals. Omitting the first five 15-minute intervals from estimationto avoid using volatility lags from the previous day has no significant effect on the results.

7Including separate average trade size variables for each trader type has little effect on the coefficientsof interest.


TABLE 3. Estimates of the Effect of Trades on Informational and Transitory Volatility.

E-MiniE-Mini S&P 500 NASDAQ-100 E-Mini Russell 2000

Panel A. Transitory Volatility

Number of tradesIndividual members (CTI 1 and 3) −0.49 (5.75)∗∗∗ −0.51 (1.94)∗ −0.07 (0.40)Member firms (CTI 2) −1.54 (11.75)∗∗∗ −1.00 (2.89)∗∗∗ −0.91 (2.78)∗∗∗

Off-exchange traders (CTI 4) 2.49 (19.14)∗∗∗ 1.54 (4.80)∗∗∗ 1.24 (4.82)∗∗∗

Average trade size −0.31 (4.67)∗∗∗ −0.48 (2.00)∗∗ 1.12 (1.00)Trend −1.81 (6.85)∗∗∗ −6.27 (9.59)∗∗∗ −1.45 (2.90)∗∗∗

Sum of 5 lagged volatilities 0.58 (700.7)∗∗∗ 0.47 (184.0)∗∗∗ 0.32 (45.5)∗∗∗

Adjusted R2 0.473 0.343 0.136Durbin-Watson d-statistic 1.998 2.000 2.003

Panel B. Informational Volatility

Number of tradesIndividual members (CTI 1 and 3) 0.07 (3.31)∗∗∗ 0.15 (1.17) −0.11 (0.90)Member firms (CTI 2) 0.26 (9.14)∗∗∗ 0.62 (4.87)∗∗∗ 0.42 (2.84)∗∗∗

Off-exchange traders (CTI 4) −0.25 (8.43)∗∗∗ −0.54 (4.67)∗∗∗ 0.01 (0.09)Average trade size −0.03 (2.43)∗∗ −0.04 (0.42) −2.10 (4.37)Trend −0.12 (2.85)∗∗∗ −0.86 (3.62)∗∗∗ −1.22 (4.31)∗∗∗

Sum of 5 lagged volatilities 0.75 (2627.9)∗∗∗ 0.67 (748.44)∗∗∗ 0.68 (516.88)∗∗∗

Adjusted R2 0.428 0.297 0.342Durbin-Watson d-statistic 1.979 2.007 2.008

Note: The estimated coefficients are for the following regression:

σ 2t = α + β0 AVt +

3∑

i=1

βi N Ti,t + τTrend +5∑

j=1

ρ j σ2t− j +

26∑

k=1

γk Dk,t + εt ,

where σ 2t is the estimate of transitory or informational volatility in 15-minute interval t, AV t is the average

trade size, NTi,t is the number of trades initiated by trader type i, Trend is a linear time trend, and Dk,tare interval dummy variables. The trend variable is calculated as the ordinal number of a given 15-minuteinterval divided by the total number of such intervals in the sample. The regression is estimated overregular trading hours from 9:30 a.m. to 4:15 p.m. ET using ordinary least squares. The sample period isfrom January 3, 2005, to December 30, 2005. The numbers of trades are standardized by dividing themby their standard deviations. All coefficients, except those for lagged volatilities, are multiplied by 106.Absolute values of t-statistics are given in parentheses. The t-statistics are computed using Newey andWest (1987) standard errors. Test statistics for lagged volatilities are Wald statistics for the hypothesis thatthe sum of five coefficients is zero.∗∗∗Significant at the 1% level.∗∗Significant at the 5% level.∗Significant at the 10% level.

who use Hasbrouck’s (1995) information share methodology. Overall, the resultsin Table 3 are consistent with exchange member firms playing an important role inprice discovery and off-exchange-initiated trades being a major source of transitoryvolatility.

In all three E-mini markets, the trend coefficients show a decline in bothtransitory and informational volatility during the sample period, perhaps driven byincreasing liquidity of these markets. The results also show evidence of persistence


in transitory volatility, with volatility lags strongly significant. This finding is con-sistent with Speight, McMillan, and Ap Gwilym (2000), who show that shocks to thetransitory volatility component are persistent at intraday frequencies. These shockscompletely decay in several hours, whereas shocks to the permanent volatilitycomponent persist much longer. Similarly, my regression estimates show strongerpersistence in informational volatility.

The usual caveat applies that correlation does not imply causality. Giventhat transitory volatility by definition is created by trading, it is reasonable toexpect causality running from the trading activity to transitory volatility. In thesubsections that follow, I use variance decomposition techniques to directly examinecontributions of different types of traders to transitory and informational volatility.

Relative Contributions of Trades to Transitory Volatility

I examine the determinants of transitory volatility using a two-stage approach, firstestimating transitory volatility using a VAR model and then regressing it on thetrading activity variables. An alternative approach is to introduce trades classifiedby trader type directly in the VAR used to estimate transitory volatility. This ap-proach allows decomposing the transitory volatility into components representingthe relative contributions of the different trader types into the variance of the pricingerror. The VAR model is specified as follows:

rt =n∑

j=1

a jrt− j +3∑

i=1

n∑

j=0

bi j xi ,t− j + vrt ,

xit =n∑

j=1

ci jrt− j +3∑

i=1

n∑

j=1

di j xi ,t− j + vit , i = 1, 2, 3. (8)

The model consists of four equations: a return equation and three signedtrade equations. Each of the trade equations represents trading activity by a giventype of traders as a function of lagged trades and price changes. The signed tradevariable for a particular trader type i is set to 1 for buys, −1 for sells, and 0 if agiven trade was initiated by one of the other types of traders. I use 15 lags of allvariables in the VAR model and 30 lags in the VMA representation. Similar to theVAR model in (3), this model uses trade price changes.

In contrast to the VAR model in (3), this model includes contemporaneoustrade variables in the return equation. E-mini futures prices tend to fluctuate ina one-tick range. Therefore, most trade price changes are bid–ask bounce. Pricechanges between the bid and ask quotes are caused by incoming market orders. Fur-thermore, all orders are executed automatically, without negotiation. Therefore, thecontemporaneous causality runs from trades to transaction price changes. Including


the contemporaneous trade variables in the return equation accounts for the contem-poraneous effect of trades on transaction prices and dramatically improves the fit ofthe equation without affecting the estimated transitory volatility. The main benefitof this modification of the model is that the covariance matrix of the disturbancesbecomes block-diagonal, allowing a clean decomposition of the transitory volatilityinto trade-related and trade-unrelated components. Specifically, the variance of thepricing error can be represented as:

σ 2s =

∞∑

j=0

α2jσ

2vr +

∞∑

j=0

β jβ ′j , (9)

where σ 2vr = var(vr,t ) and is the covariance matrix of the trade disturbances.

The α and β coefficients are calculated using the VMA coefficients followingHasbrouck (1993). The first term in equation (9) represents the component of thepricing error variance contributed by return innovations. The second term is thetrade-related component of the transitory volatility. is generally not diagonal.Therefore, the components of the pricing error variance attributable to each of thethree trader types, or “noise shares,” can be computed after transforming into alower triangular matrix F by the Cholesky factorization, = FF′, as follows:

Si =

∞∑

j=0

([β j F]i )2

σ 2s

, (10)

where [β jF]i is the ith element of the row matrix β jF. The result of this variancedecomposition depends on ordering of the trade variables in the model. Similar toHasbrouck’s (1995) calculation of information shares, the upper and lower boundsof the noise shares can be computed by permuting the order of the trade variablesin and β j.

The estimated noise shares are reported in Panel A of Table 4. For all threemarkets, trades initiated by off-exchange traders account for the largest percentageof the pricing error variance. The midpoints of the upper and lower bounds of thenoise shares range from about 44% in the E-mini Russell 2000 market to about 65%in the E-mini S&P 500 market. The corresponding contributions of individual andinstitutional exchange members are much smaller despite the fact that exchangemember firms account for a larger share of the trading activity.

To simplify comparison of the noise shares across the trader types, I com-pute the ratios of the noise share midpoints to the percentage of trades initiated bya particular trader type. The ratios reported in Panel B of Table 4 show that, con-trolling for the amount of trading activity, trades initiated by off-exchange tradersare associated with at least twice as much transitory volatility as trades initiated by


TABLE 4. Noise Share Statistics.

Panel A. Noise Shares of Trades Initiated by Individual Exchange Members (CTI 1 and 3), ExchangeMember Firms (CTI 2), and Off-Exchange Traders (CTI 4)

Midpoint of Upper andUpper Bound Lower Bound Lower Bounds

CTI 1 & 3 CTI 2 CTI 4 CTI 1 & 3 CTI 2 CTI 4 CTI 1 & 3 CTI 2 CTI 4

E-mini S&P 500 futuresMedian 11.0% 21.7% 69.3% 4.7% 13.2% 60.3% 7.8% 17.4% 64.8%Mean 11.1% 21.8% 69.7% 4.6% 13.2% 60.6% 7.8% 17.6% 65.1%Std. error of mean 0.11% 0.17% 0.20% 0.06% 0.14% 0.17% 0.08% 0.15% 0.18%

E-mini NASDAQ-100 futuresMedian 8.1% 23.1% 51.7% 2.9% 14.5% 45.1% 5.5% 18.8% 48.4%Mean 8.0% 22.6% 52.0% 2.7% 14.0% 45.5% 5.4% 18.3% 48.8%Std. error of mean 0.11% 0.21% 0.47% 0.07% 0.18% 0.45% 0.08% 0.19% 0.45%

E-mini Russell 2000 futuresMedian 4.7% 25.0% 44.6% 3.2% 23.6% 43.6% 3.9% 24.3% 44.1%Mean 4.6% 24.4% 44.4% 3.0% 23.1% 43.6% 3.8% 23.7% 44.0%Std. error of mean 0.11% 0.47% 0.32% 0.09% 0.46% 0.32% 0.10% 0.46% 0.32%

Panel B. Noise Shares—Controlling for Proportion of Trades

Individual Members Member Firms Off-Exchange Traders(CTI 1 and 3) (CTI 2) (CTI 4)

E-mini S&P 500 0.47 (0.007) 0.39 (0.003) 1.74 (0.007)E-mini NASDAQ-100 0.33 (0.006) 0.33 (0.003) 1.90 (0.022)E-mini Russell 2000 0.34 (0.008) 0.50 (0.010) 1.12 (0.011)

Note: The statistics are for regular trading hours from 9:30 a.m. to 4:15 p.m. ET. The sample period is fromJanuary 3, 2005, to December 30, 2005. Panel A reports “noise shares,” that is, proportional contributions oftrades initiated by different trader types to the pricing error variance. The noise shares are estimated separatelyfor each day in the sample. The average contributions of return innovations to the pricing error variance in theS&P 500, NASDAQ-100, and Russell 2000 E-mini markets are 10.6%, 28.0%, and 27.8%, respectively. PanelB presents the mean ratios of the noise share midpoint to the proportion of trades initiated by the particulartrader type on a given day. Standard errors of means are shown in parentheses. All ratios reported in the table aresignificantly different from 1 at the 1% level.

exchange members. The difference between exchange members and off-exchangetraders is especially pronounced in the E-mini NASDAQ-100 market, where off-exchange-initiated trades are associated with about six times as much transitoryvolatility as exchange member trades. Overall, the results in Table 4 are consistentwith the regression results presented in Table 3 and provide additional support toHypothesis 1.

I have also estimated the “noise shares” for the after-hours trading. Theresults are qualitatively similar to the results for the regular trading hours reportedin Table 4, with off-exchange traders contributing a disproportionately large shareof transitory volatility.8 Because the information share results discussed in the nextsection show that no trader type is clearly more informed in after-hours trading, the

8The noise shares for after-hours trading are not tabulated to conserve space but are available uponrequest.


differential effects of trader types on transitory volatility are likely to be explainedby different trading strategies rather than by informational differences.

Informational Contributions

To test Hypothesis 2 relating to the relative information content of trades, I examineinformational contributions of the three types of traders using Hasbrouck’s (1995)model. This model estimates “information shares” as relative contributions of sev-eral cointegrated price series in the efficient price variance. The information sharesare estimated using a vector error correction model (VECM).9 Hasbrouck’s modelproduces estimates of the upper and lower bounds of the information shares. Thesebounds may diverge substantially when the price innovations are correlated acrossthe VECM equations. Using higher frequency price series reduces the correlationof innovations across the VECM equations and allows for a more precise identi-fication of the information shares. As input data for the VECM analysis, I use amatched trade-by-trade price series of trades initiated by the three trader types.10 Tosynchronize the price series, for each trade initiated by a given type of trader I usethe last available trade prices for the other trader types. This matching procedureallows preserving the exact sequence of trades in the data.11 Information shares arecalculated separately for the regular trading hours and after-hours trading for eachday in the sample and then averaged across days.

In addition to the analysis of the regular exchange trading hours, I look atafter-hours trading to shed some light on the possible reasons for the informationaldifferences among trader types. After-hours trading is different from trading duringthe regular hours in two important respects. First, the CME trading floor is closedand exchange members are unable to use their exclusive access to floor trading.Second, the major stock markets are also closed, although a small amount of trad-ing takes place on electronic communication networks (ECNs) from 4:00 p.m. to8:00 p.m. and from 7:00 a.m. to 9:30 a.m. Large institutional traders (exchangemember firms) have better access to real-time information about stock prices thando individual traders or smaller institutions. Such information is generally unavail-able after hours.

The estimated information shares are shown in Table 5. During the trad-ing day, exchange member firms contribute most of the information in all three

9Detailed discussion of Hasbrouck’s (1995) methodology is omitted for brevity.10I employ 100 lags in the VECM for all three markets when the model is estimated during regular

trading hours. During the after-hours trading period, I use 30 lags for the E-mini S&P 500, 15 lags for theE-mini NASDAQ-100, and 5 lags for the E-mini Russell 2000 futures, respectively, to account for differentlevels of trading activity.

11Kurov and Lasser (2004) use a one-second equally spaced price series in the VECM. That approacheliminates a large number of trades and does not preserve the sequence of trades that occurred within thesame second. My trade matching procedure preserves the exact sequence of trades and significantly reducesthe residual correlation in the VECM, allowing for a more accurate identification of the information shares.


TABLE 5. Information Share Statistics of Trades Initiated by Individual Exchange Members (CTI1 and 3), Exchange Member Firms (CTI 2), and Off-Exchange Traders (CTI 4).

Midpoint of Upper andUpper Bound Lower Bound Lower Bounds

CTI 1 & 3 CTI 2 CTI 4 CTI 1 & 3 CTI 2 CTI 4 CTI 1 & 3 CTI 2 CTI 4

Panel A. Regular Trading Hours

E-mini S&P 500 futuresMedian 11.3% 63.4% 27.4% 10.3% 61.0% 25.0% 10.9% 62.2% 26.2%Mean 12.8% 62.1% 28.0% 11.8% 59.7% 25.7% 12.3% 60.9% 26.9%Std. error 0.40% 0.59% 0.41% 0.38% 0.59% 0.40% 0.39% 0.59% 0.40%

of mean

E-mini NASDAQ-100 futuresMedian 10.4% 75.7% 15.6% 9.5% 73.4% 13.9% 9.9% 74.6% 14.7%Mean 10.9% 75.0% 16.5% 10.0% 72.7% 14.9% 10.4% 73.9% 15.7%Std. error 0.32% 0.51% 0.46% 0.31% 0.52% 0.44% 0.31% 0.51% 0.45%

of mean

E-mini Russell 2000 futuresMedian 4.4% 72.3% 26.7% 4.1% 69.1% 23.8% 4.2% 70.7% 25.3%Mean 5.1% 70.2% 27.8% 4.8% 67.2% 24.9% 4.9% 68.7% 26.4%Std. error 0.21% 0.63% 0.63% 0.20% 0.65% 0.60% 0.21% 0.64% 0.62%

of mean

Panel B. After-Hours Trading

E-mini S&P 500 futuresMedian 21.7% 25.9% 52.4% 19.8% 24.0% 49.3% 20.8% 24.9% 50.9%Mean 23.2% 27.8% 52.4% 21.2% 26.0% 49.4% 22.2% 26.9% 50.9%Std. error 0.76% 1.02% 0.96% 0.73% 0.98% 0.96% 0.74% 1.00% 0.96%

of mean

E-mini NASDAQ-100 futuresMedian 13.6% 37.7% 43.6% 12.3% 35.1% 40.4% 13.0% 36.4% 42.0%Mean 19.5% 38.0% 45.8% 18.0% 35.6% 43.0% 18.8% 36.8% 44.4%Std. error 1.18% 1.64% 1.61% 1.13% 1.60% 1.59% 1.15% 1.62% 1.60%

of mean

E-mini Russell 2000 futuresMedian 13.4% 13.6% 63.3% 11.8% 11.5% 59.0% 12.9% 12.7% 60.7%Mean 21.5% 22.7% 59.2% 19.8% 20.8% 56.0% 20.6% 21.8% 57.6%Std. error 1.42% 1.54% 1.74% 1.36% 1.49% 1.75% 1.39% 1.51% 1.75%

of mean

Note: The regular trading hours are from 9:30 a.m. to 4:15 p.m. ET. The after-hours trading period is from6:00 p.m. to 9:30 a.m. ET the following day. The sample period is from January 3, 2005, to December 30,2005. Information shares are calculated separately for each day in the sample and then averaged across days.

markets. The average midpoints of information shares for trades initiated by ex-change member firms range from about 61% for the E-mini S&P 500 futures toabout 74% for the E-mini NASDAQ-100 futures. The information shares of tradesinitiated by individual exchange members are modest, with mean information sharemidpoints ranging from about 5% in the E-mini Russell 2000 market to about 12%


in the E-mini S&P 500 market. Finally, the information share midpoints of off-exchange-initiated trades range from about 16% to about 27% and exceed thoseof the individual members’ trades in all three markets. In contrast, off-exchangetraders have the largest information shares in after-hours trading, consistent withtheir larger share in the trading activity.

Using a four-month sample period from early May to early September 2001,Kurov and Lasser (2004) report trivial information shares for clearing membertrades. Although the CTI 2 category is now somewhat more inclusive than it wasin 2001, comparing my information share results with those of Kurov and Lassershows a significant change in the relative informational contributions of differenttrader types in recent years. Far from being uninformed hedgers, exchange memberfirms now appear to play a key role in price discovery in the E-mini index futuresmarkets.12 A possible reason for the growing informational role of institutionaltraders is their increased use of algorithmic trading systems, allowing them toexploit short-lived arbitrage opportunities and trade on electronically disseminatednews faster than other traders.

To control for the proportion of trading activity initiated by different tradertypes, I calculate the ratios of the information share midpoints to the percentageof trades initiated by a given trader category. The ratios are calculated on a dailybasis and reported in Panel A of Table 6. During the trading day, the ratios of theinformation share midpoint to the percentage of all trades are significantly higherthan one for trades initiated by exchange member firms. This result shows that themember firms’ trades are the more informative than trades of other traders. Theinformation shares of trades initiated by other traders are significantly below theirrespective proportions of initiated trades. During after-hours trading, however, theratio is greater than one for off-exchange traders and less than one for exchangemember firms.

Information shares represent the proportional contributions of price inno-vations originating from a particular category of trades to the innovations in thecommon efficient price. Therefore, it is interesting to examine the relation betweeninformation shares and the percentages of price-setting trades initiated by differenttrader types. I define price-setting trades as buy trades with a higher price than theprice of the previous buy trade or sell trades with a lower price than the price of theprevious sell trade. Coval and Shumway (2005) use a similar approach to identifyprice-setting trades.

The ratios of information shares to percentages of price-setting trades ini-tiated by a given trader type are reported in Panel B of Table 6. In all three markets,

12I also estimated information shares for the three trader types using my trade-matching procedureand the sample period of Kurov and Lasser (2004). The results were qualitatively similar to their results.Therefore, the differences between my information share results and theirs are not due to different trade-matching procedures.


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these ratios increase for trades initiated by exchange member firms and individualexchange members compared to the ratios based on the total number of initiatedtrades. Trades of exchange member firms still appear to be the most informed dur-ing the regular trading hours. In the S&P 500 and NASDAQ-100 E-mini markets,the price-setting trades initiated by individual members are more informative thanoff-exchange-initiated trades. For off-exchange traders during the regular tradinghours, the ratios of information shares to the proportion of price-setting trades aremuch lower than one, indicating that these traders initiate a disproportionately highpercentage of price-setting trades that have relatively little long-term effect on theprices.

To provide an additional test of the relative information content of tradesinitiated by the three types of traders, I calculate the cumulative impulse responsefunctions by forecasting the VECM after a unit shock to one of the trade priceseries. Figure I shows the impulse responses for the regular trading hours. For

Shock to Prices of Trades Initiated by Individual Exchange Members (CTI 1 and 3)

Shock to Prices of Trades Initiated by Exchange Member Firms (CTI 2)

Shock to Prices of Trades Initiated by Off-Exchange Customers (CTI 4)

Panel A. E-Mini S&P 500 Futures

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades

CTI 1 & 3 CTI 2 CTI 4

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades


0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades


Panel B. E-Mini NASDAQ -100 Futures

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades


0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades


0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades


Panel C. E-Mini Russell 2000 Futures

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades


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0.2

0.4

0.6

0.8

1.0

0 50 100 150

Trades


0.0

0.2

0.4

0.6

0.8

1.0

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Figure I. Cumulative Impulse Response (Price Impact) Functions. Impulse responses are calculatedseparately for the regular trading hours of each day in the sample and then averaged across days.CTI 1 and 3 are individual exchange members, CTI 2 are exchange member firms, and CTI 4are off-exchange customers.


TABLE 7. Coefficients of the Efficient Price (Long-Run Cumulative Price Effects) for TradesInitiated by Individual Exchange Members (CTI 1 and 3), Exchange Member Firms(CTI 2), and Off-Exchange Traders (CTI 4).

Regular Trading Hours After-Hours Trading

CTI 1 and 3 CTI 2 CTI 4 CTI 1 and 3 CTI 2 CTI 4

E-mini S&P 500 futuresMean 0.215 0.363 0.164 0.339 0.331 0.334Std. error of mean 0.003 0.003 0.002 0.007 0.009 0.004

E-mini NASDAQ-100 futuresMean 0.253 0.508 0.199 0.348 0.399 0.407Std. error of mean 0.004 0.003 0.003 0.011 0.012 0.007

E-mini Russell 2000 futuresMean 0.193 0.675 0.326 0.326 0.275 0.541Std. error of mean 0.005 0.004 0.005 0.021 0.021 0.015

Note: The regular trading hours are from 9:30 a.m. to 4:15 p.m. ET. The after-hours trading period is from6:00 p.m. to 9:30 a.m. ET the following day. The sample period is from January 3, 2005, to December30, 2005. The long-run cumulative price effects are measured 500 trades after the price shocks to allowforecasted prices to fully converge. Coefficients of the efficient price are calculated separately for eachday in the sample and then averaged across days.

all three markets, the long-run values of the cumulative impulse responses aremuch larger for unit shocks to the prices of trades initiated by exchange memberfirms than for similar innovations initiated by individual exchange members or off-exchange traders. For the S&P 500 and NASDAQ-100 E-mini futures, individualmember trades are more informative than trades of off-exchange traders, whereas inthe E-mini Russell 2000 market off-exchange-initiated trades are somewhat moreinformative. In all three markets, there is a small initial overreaction of off-exchangetraders to price shocks initiated by other trader types.

Table 7 reports the coefficients of the efficient price, which are identical tothe long-run values of the cumulative impulse responses. The cumulative impulseresponses provide additional support to the information share results, showing thatexchange member firms contribute more to price discovery than other traders duringthe regular trading hours. Overall, the results for the trading day reported in Tables5, 6, and 7 are consistent with expectations expressed in Hypothesis 2.

Based on the cumulative impulse responses, trades of individual exchangemembers and off-exchange traders are more informative after hours than during thetrading day. The increased information content of after-hours trades is consistentwith Barclay and Hendershott (2003). Because the information content of tradesinitiated by individual exchange members increases after hours, access to the trad-ing floor is unlikely to be a key factor influencing their trading. Table 7 shows thatthe permanent price effects in the S&P 500 and NASDAQ-100 E-mini markets aresimilar across the trader types in after-hours trading. In the E-mini Russell 2000market, off-exchange-initiated trades are the most informative. Furthermore, the


information content of institutional member trades declines in after-hours tradingrelative to the trading day. This result shows that exchange member firms no longerhave an informational advantage after hours. The key difference between the twotrading periods is lack of active trading in the underlying stocks after hours. There-fore, real-time access to the underlying stock prices is likely to be an importantsource of informational advantage that exchange member firms have during thetrading day.

Daigler and Wiley (1999) argue that off-exchange traders fit the descrip-tion of noise traders, and my results confirm that these traders introduce noise intothe prices. However, the information share results show that their trades are alsoa source of information, especially in after-hours trading. Although this conclu-sion may appear counterintuitive, it is not inconsistent with microstructure the-ory. For example, Hasbrouck (1993, p. 199) specifies a structure of the efficientprice innovation and the pricing error that implies that a trade can contain in-formation and simultaneously push the security price away from the fundamentalvalue.

VI. Conclusion

I examine the effects on transitory volatility and informational contributions oftrades initiated by different types of traders in three E-mini index futures markets.The empirical results show a strong positive relation between transitory volatil-ity estimated using the Hasbrouck (1993) model and trading activity initiated byoff-exchange traders. Trading activity initiated by exchange members tends to benegatively related to transitory volatility. These results, supported by results froma VAR-based decomposition of transitory volatility, show that trades initiated byoff-exchange traders induce more transitory volatility than trades initiated by ex-change members. Although this conclusion is broadly consistent with Daigler andWiley (1999), I provide direct evidence on the effect of different trader types ontransitory volatility.

Exchange locals are usually viewed as better informed traders who playan important role in price formation (e.g., Kurov and Lasser 2004). My resultsconfirm that trades initiated by local traders contain information and contribute toprice discovery. I also find, however, that exchange member firms appear to be moreinformed and contribute more to price discovery than individual exchange membersor off-exchange traders. During the trading day, trades of member firms accountfor between 61% and 74% of price discovery. These information shares exceedthe proportions of trading activity generated by member firms. Taken together, theresults show that institutional traders are more informed than other types of tradersin anonymous electronic trading. I also find that the informational advantage ofinstitutional traders disappears in after-hours trading when they do not have access to


real-time information about the underlying stock prices. My findings are importantbecause they contribute to better understanding of the effects of trader heterogeneityon price formation.

Trading is a zero sum game, with informed traders making profits at the ex-pense of the uninformed. It would be interesting to see if the apparent informationaladvantage of institutions translates into trading profits. I considered estimating trad-ing profits assuming that all trades are closed out at the settlement price. However,profit estimates based on this assumption are likely to be very imprecise. Becausemy data set does not allow tracking positions for each trader’s account, I leave theanalysis of trading profits across trader types for future research.

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